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Pregled bibliografske jedinice broj: 1038942

Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning


Visković, Lucija; Nižetić Kosović, Ivana; Mastelić, Toni
Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning // Proceedings of International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2019)
Split, Hrvatska, 2019. str. - doi:10.23919/SOFTCOM.2019.8903738 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1038942 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning

Autori
Visković, Lucija ; Nižetić Kosović, Ivana ; Mastelić, Toni

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2019) / - , 2019

Skup
2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

Mjesto i datum
Split, Hrvatska, 19.07.2019. - 21.07.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
remote sensing ; satellite images ; land usage ; crop classification ; machine learning

Sažetak
Satellite images are highly utilized for detecting land usage, while in recent years a finer-grade crop classification has become important in the context of precision agriculture. However, such classification brings new challenges, which aside from multi- spectral images require exploitation of their multi-temporal properties as well, with pixel- based analysis and larger number of classes. In this paper, we apply several machine learning algorithms on multi-spectral and multi-temporal satellite images and derive crop classification models. The models are applied only on agricultural fields, which can be singled out with the existing land usage classification models. Results show that the random forest outperforms other algorithms with accuracy score of 0.8420 and Kappa score of 0.8157. Detailed analysis of recall and precision scores is given for each crop separately, followed by a comprehensive discussion.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove:
Ericsson Nikola Tesla d.d.

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Visković, Lucija; Nižetić Kosović, Ivana; Mastelić, Toni
Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning // Proceedings of International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2019)
Split, Hrvatska, 2019. str. - doi:10.23919/SOFTCOM.2019.8903738 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Visković, L., Nižetić Kosović, I. & Mastelić, T. (2019) Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning. U: Proceedings of International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2019) doi:10.23919/SOFTCOM.2019.8903738.
@article{article, author = {Viskovi\'{c}, Lucija and Ni\v{z}eti\'{c} Kosovi\'{c}, Ivana and Masteli\'{c}, Toni}, year = {2019}, pages = {---}, DOI = {10.23919/SOFTCOM.2019.8903738}, keywords = {remote sensing, satellite images, land usage, crop classification, machine learning}, doi = {10.23919/SOFTCOM.2019.8903738}, title = {Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning}, keyword = {remote sensing, satellite images, land usage, crop classification, machine learning}, publisherplace = {Split, Hrvatska} }
@article{article, author = {Viskovi\'{c}, Lucija and Ni\v{z}eti\'{c} Kosovi\'{c}, Ivana and Masteli\'{c}, Toni}, year = {2019}, pages = {---}, DOI = {10.23919/SOFTCOM.2019.8903738}, keywords = {remote sensing, satellite images, land usage, crop classification, machine learning}, doi = {10.23919/SOFTCOM.2019.8903738}, title = {Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning}, keyword = {remote sensing, satellite images, land usage, crop classification, machine learning}, publisherplace = {Split, Hrvatska} }

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